Editor
From Cover to Cover

Spectral fingerprinting of ovarian cancer in serum samples

Ovarian cancer can be predicted with high sensitivity and specificity via a fingerprint obtained, via machine learning, from near-infrared fluorescence emissions of an array of carbon nanotube sensors in serum samples.
Published
Go to the profile of Pep Pàmies
Pep Pàmies
Chief Editor, Nature Biomedical Engineering, Nature Research
Like
Go to the profile of Evelina Satkevic
Go to the profile of Olga Kharchenko

Read the paper

Nature
Nature Nature

Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning - Nature Biomedical Engineering

Ovarian cancer can be predicted with high sensitivity and specificity via a fingerprint obtained, via machine learning, from near-infrared fluorescence emissions of an array of carbon nanotube sensors in serum samples.

The cover illustrates that the analysis, via machine learning, of near-infrared-fluorescence emissions of carbon-nanotube sensors placed in serum samples can be used to predict ovarian cancer.

See Kim et al.

Image: Olga Kharchenko. Cover design: Alex Wing.
Go to the profile of Pep Pàmies
Pep Pàmies
Chief Editor, Nature Biomedical Engineering, Nature Research

Please sign in or register for FREE

If you are a registered user on Biotechnology and Bioengineering Community, please sign in

Sign In Register

Recommended Content

Cookies

We use cookies to ensure the functionality of our website, to personalize content and advertising, to provide social media features, and to analyze our traffic. If you allow us to do so, we also inform our social media, advertising and analysis partners about your use of our website. You can decide for yourself which categories you want to deny or allow. Please note that based on your settings not all functionalities of the site are available.

Further information can be found in our privacy policy.